{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:47:46Z","timestamp":1792511266523,"version":"4.5.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Continuous glucose monitoring (CGM) sensors are increasingly used to identify and manage post-bariatric hypoglycemia (PBH) and to support decision support systems (DSSs) for proactive glucose management. Despite meal timing being key information for these systems, automated, wearable-based meal detection remains an unmet clinical need.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We present a real-time meal detection algorithm for individuals with PBH that combines CGM and heart rate (HR) signals. The algorithm is a heuristic decision-tree model based on four individualized features extracted from CGM (rate of change, glucose relative excursion, glucose peak value) and HR (peak value). It was developed and tested using a dataset of 40 PBH patients monitored for up to 50 days with a Dexcom G6 CGM and a Garmin Venu Sq smartwatch, and its performance was evaluated in both controlled and free-living conditions, benchmarked against state-of-the-art CGM-only meal detection methods for the PBH population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The algorithm achieved 100% recall in the controlled setting and, in free-living conditions, an average precision of 85% and recall of 78%. It also reduced false positives compared with CGM-only algorithms (one every 2.3 days vs. one every 1.3 days).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Eliminating the need for manual meal announcement, the proposed algorithm overcomes a key barrier to fully automated glucose management, reducing patient burden while maintaining reliable detection performance even in unstructured, free-living conditions. These results support the integration of the algorithm into DSSs for PBH and other populations, where timely and accurate meal detection is critical.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1758474","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T12:05:08Z","timestamp":1782907508000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A personalized and automated real-time meal detection algorithm based on continuous glucose monitoring and heart rate data for individuals with post-bariatric hypoglycemia"],"prefix":"10.3389","volume":"8","author":[{"given":"Luca","family":"Cossu","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova","place":["Padova, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Cappon","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova","place":["Padova, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Wortmann","sequence":"additional","affiliation":[{"name":"Universit\u00e4t St. Gallen","place":["Z\u00fcrich, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Herzig","sequence":"additional","affiliation":[{"name":"Department of Diabetes Endocrinology Nutritional Medicine and Metabolism Inselspittal, Bern University Hospital University of Bern","place":["Bern, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lia","family":"Bally","sequence":"additional","affiliation":[{"name":"Department of Diabetes Endocrinology Nutritional Medicine and Metabolism Inselspittal, Bern University Hospital University of 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